Usage
kern.B(x, xi, h, g = 0)
kern.C(x, xi, h)
kern.G(x, xi, h)
kern.O(x, xi, h)
kern.T(x, xi, h)
kfoldCV(h, x, nbsets = 2, w = rep(1, length(x)), lower = mean(x) - 5*sd(x), upper = mean(x) + 5*sd(x))
npMSL_old(x, mu0, blockid = 1:ncol(x), bw=bw.nrd0(as.vector(as.matrix(x))), samebw = TRUE, h=bw, eps=1e-8, maxiter=500, bwiter = maxiter, ngrid = 200, post = NULL, verb = TRUE)
splitsample(n, nbsets = 2)
wbw.kCV(x, nbfold = 5, w = rep(1, length(x)), hmin = 0.1*hmax, hmax = NULL)
Arguments
x
A vector of values to which local modeling techniques are applied.
xi
An n-vector of data values.
h
The bandwidth controlling the size of the window used for the
local estimation around x.
g
A shape parameter required for the symmetric beta kernel. The default
is g = 0 which yields the uniform kernel. Some common values are g = 1 for the
Epanechnikov kernel, g = 2 for the biweight kernel, and g = 3 for the triweight kernel.
mu0
See updated arguments in the npMSL function.
blockid
See updated arguments in the npMSL function.
bw
See updated arguments in the npMSL function.
samebw
See updated arguments in the npMSL function.
h
See updated arguments in the npMSL function.
eps
See updated arguments in the npMSL function.
maxiter
See updated arguments in the npMSL function.
bwiter
See updated arguments in the npMSL function.
ngrid
See updated arguments in the npMSL function.
post
See updated arguments in the npMSL function.
verb
See updated arguments in the npMSL function.
n
See updated arguments in the npMSL function.
nbsets
See updated arguments in the npMSL function.
w
See updated arguments in the npMSL function.
lower
See updated arguments in the npMSL function.
upper
See updated arguments in the npMSL function.
nbfold
See updated arguments in the npMSL function.
hmin
See updated arguments in the npMSL function.
hmax
See updated arguments in the npMSL function.